Search Results for author: Aneta Siemiginowska

Found 4 papers, 2 papers with code

Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era

no code implementations • 5 Nov 2019 • Brian Nord, Andrew J. Connolly, Jamie Kinney, Jeremy Kubica, Gautaum Narayan, Joshua E. G. Peek, Chad Schafer, Erik J. Tollerud, Camille Avestruz, G. Jogesh Babu, Simon Birrer, Douglas Burke, João Caldeira, Douglas A. Caldwell, Joleen K. Carlberg, Yen-Chi Chen, Chuanfei Dong, Eric D. Feigelson, V. Zach Golkhou, Vinay Kashyap, T. S. Li, Thomas Loredo, Luisa Lucie-Smith, Kaisey S. Mandel, J. R. Martínez-Galarza, Adam A. Miller, Priyamvada Natarajan, Michelle Ntampaka, Andy Ptak, David Rapetti, Lior Shamir, Aneta Siemiginowska, Brigitta M. Sipőcz, Arfon M. Smith, Nhan Tran, Ricardo Vilalta, Lucianne M. Walkowicz, John ZuHone

The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration.

Astronomy scientific discovery

Paper
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The Role of Machine Learning in the Next Decade of Cosmology

no code implementations • 26 Feb 2019 • Michelle Ntampaka, Camille Avestruz, Steven Boada, Joao Caldeira, Jessi Cisewski-Kehe, Rosanne Di Stefano, Cora Dvorkin, August E. Evrard, Arya Farahi, Doug Finkbeiner, Shy Genel, Alyssa Goodman, Andy Goulding, Shirley Ho, Arthur Kosowsky, Paul La Plante, Francois Lanusse, Michelle Lochner, Rachel Mandelbaum, Daisuke Nagai, Jeffrey A. Newman, Brian Nord, J. E. G. Peek, Austin Peel, Barnabas Poczos, Markus Michael Rau, Aneta Siemiginowska, Dougal J. Sutherland, Hy Trac, Benjamin Wandelt

In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data.

Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics

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Flexible and Scalable Methods for Quantifying Stochastic Variability in the Era of Massive Time-Domain Astronomical Data Sets

2 code implementations • 24 Feb 2014 • Brandon C. Kelly, Andrew C. Becker, Malgosia Sobolewska, Aneta Siemiginowska, Phil Uttley

We present the use of continuous-time autoregressive moving average (CARMA) models as a method for estimating the variability features of a light curve, and in particular its power spectral density (PSD).

Instrumentation and Methods for Astrophysics

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Sherpa: a Mission-Independent Data Analysis Application

1 code implementation • 27 Aug 2001 • Peter E. Freeman, Stephen Doe, Aneta Siemiginowska

The ever-increasing quality and complexity of astronomical data underscores the need for new and powerful data analysis applications.

parameter estimation

158

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